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Record W4404377080 · doi:10.1063/5.0235224

Rheological behavior of amine-functionalized liquid polybutadiene

2024· article· en· W4404377080 on OpenAlexafffund
Amir Malmir, Saeed Ataie, Benjamin M. Yavitt, Laurel L. Schafer, Savvas G. Hatzikiriakos

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversity of British Columbia
FundersRES’EAU-WaterNET
KeywordsPhysicsPolybutadieneRheologyAmine gas treatingPolymer scienceChemical engineeringThermodynamicsOrganic chemistryPolymerCopolymerNuclear magnetic resonanceChemistry

Abstract

fetched live from OpenAlex

Varying quantities of hydrogen-bonding stickers (amines) are introduced to liquid polybutadiene through the hydroaminoalkylation method. Amination significantly affects both the glassy and rubbery dynamics of these materials. The amination process results in a delay of the transition from the glassy to rubbery state, attributed to the lower mobility of hydrogen-bonding sites compared to the backbone segments. As the density of stickers increases, a liquid to solid transition is observed, and the emergence of a plateau rubbery modulus due to hydrogen bonding between amine functional groups. Rheological analysis reveals a failure in time-temperature superposition near the gel point. This failure is due to the decrease in the intensity of the elastically effective network strands with rising temperature from the weakening of the strength of hydrogen bonds. Moreover, the terminal relaxation timescale lengthens considerably as the degree of gelation increases, indicating the impact of many cooperative intermolecular associations. Compared to high molecular weight polybutadiene, the functionalized low molecular weight polybutadienes possess similar plateau modulus, highlighting the effectiveness of post-polymerization modifications in enhancing the mechanical properties of the amine-functionalized low molecular weight polybutadiene.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.273
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2024
Admission routes2
Has abstractyes

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